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The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis

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The effectiveness of technology-supported personalised learning in low- and middle-income countries : A meta-analysis. / Major, Louis; Francis, Gill A.; Tsapali, Maria.

In: British Journal of Educational Technology, 24.05.2021.

Research output: Contribution to journalReview articlepeer-review

Harvard

Major, L, Francis, GA & Tsapali, M 2021, 'The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis', British Journal of Educational Technology. https://doi.org/10.1111/bjet.13116

APA

Major, L., Francis, G. A., & Tsapali, M. (2021). The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13116

Vancouver

Major L, Francis GA, Tsapali M. The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis. British Journal of Educational Technology. 2021 May 24. https://doi.org/10.1111/bjet.13116

Author

Major, Louis ; Francis, Gill A. ; Tsapali, Maria. / The effectiveness of technology-supported personalised learning in low- and middle-income countries : A meta-analysis. In: British Journal of Educational Technology. 2021.

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@article{bcf56d1dc74944e5a03518180b575298,
title = "The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis",
abstract = "Digital technology offers the potential to address educational challenges in resource-poor settings. This meta-analysis examines the impact of students' use of technology that personalises and adapts to learning level in low- and middle-income countries. Following a systematic search for research between 2007 and 2020, 16 randomised controlled trials were identified in five countries. Studies involved 53,029 learners aged 6–15 years. Coding examined learning domain (mathematics and literacy); personalisation level and delivery; technology use; and intervention duration and intensity. Overall, technology-supported personalised learning was found to have a statistically significant—if moderate—positive effect size of 0.18 on learning (p = 0.001). Meta-regression reveals how more personalised approaches which adapt or adjust to learners' level led to significantly greater impact (an effect size of 0.35) than those only linking to learners' interests or providing personalised feedback, support, and/or assessment. Avenues for future research include investigating cost implications, optimum programme length, and teachers' role in making personalised learning with technology effective. Practitioner notes What is already known about this topic? Promoting personalised learning is an established aim of educators. Using technology to support personalised learning in low- and middle-income countries (LMICs) could play an important role in ensuring more inclusive and equitable access to education, particularly in the aftermath of COVID-19. There is currently no rigorous overview of evidence on the effectiveness of using technology to enable personalised learning in LMICs. What this paper adds? The meta-analysis is the first to evaluate the effectiveness of technology-supported personalised learning in improving learning outcomes for school-aged children in LMICs. Technology-supported personalised learning has a statistically significant, positive effect on learning outcomes. Interventions are similarly effective for mathematics and literacy and whether or not teachers also have an active role in the personalisation. Personalised approaches that adapt or adjust to the learner led to significantly greater impact, although whether these warrant the additional investment likely necessary for implementation at scale needs to be investigated. Personalised technology implementation of moderate duration and intensity had similar positive effects to that of stronger duration and intensity, although further research is needed to confirm this. Implications for practice and/or policy: The inclusion of more adaptive personalisation features in technology-assisted learning environments can lead to greater learning gains. Personalised technology approaches featuring moderate personalisation may also yield learning rewards. While it is not known whether personalised technology can be scaled in a cost-effective and contextually appropriate way, there are indications that this is possible. The appropriateness of teachers integrating personalised approaches in their practice should be explored given {\textquoteleft}supplementary{\textquoteright} uses of personalised technology (ie, additional sessions involving technology outside of regular instruction) are common.",
keywords = "computer-assisted learning, learning outcomes, low- and middle-income, meta-analysis, personalisation, personalised adaptive learning",
author = "Louis Major and Francis, {Gill A.} and Maria Tsapali",
note = "{\textcopyright} 2021 The Authors",
year = "2021",
month = may,
day = "24",
doi = "10.1111/bjet.13116",
language = "English",
journal = "British Journal of Educational Technology",
issn = "0007-1013",
publisher = "Wiley",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - The effectiveness of technology-supported personalised learning in low- and middle-income countries

T2 - A meta-analysis

AU - Major, Louis

AU - Francis, Gill A.

AU - Tsapali, Maria

N1 - © 2021 The Authors

PY - 2021/5/24

Y1 - 2021/5/24

N2 - Digital technology offers the potential to address educational challenges in resource-poor settings. This meta-analysis examines the impact of students' use of technology that personalises and adapts to learning level in low- and middle-income countries. Following a systematic search for research between 2007 and 2020, 16 randomised controlled trials were identified in five countries. Studies involved 53,029 learners aged 6–15 years. Coding examined learning domain (mathematics and literacy); personalisation level and delivery; technology use; and intervention duration and intensity. Overall, technology-supported personalised learning was found to have a statistically significant—if moderate—positive effect size of 0.18 on learning (p = 0.001). Meta-regression reveals how more personalised approaches which adapt or adjust to learners' level led to significantly greater impact (an effect size of 0.35) than those only linking to learners' interests or providing personalised feedback, support, and/or assessment. Avenues for future research include investigating cost implications, optimum programme length, and teachers' role in making personalised learning with technology effective. Practitioner notes What is already known about this topic? Promoting personalised learning is an established aim of educators. Using technology to support personalised learning in low- and middle-income countries (LMICs) could play an important role in ensuring more inclusive and equitable access to education, particularly in the aftermath of COVID-19. There is currently no rigorous overview of evidence on the effectiveness of using technology to enable personalised learning in LMICs. What this paper adds? The meta-analysis is the first to evaluate the effectiveness of technology-supported personalised learning in improving learning outcomes for school-aged children in LMICs. Technology-supported personalised learning has a statistically significant, positive effect on learning outcomes. Interventions are similarly effective for mathematics and literacy and whether or not teachers also have an active role in the personalisation. Personalised approaches that adapt or adjust to the learner led to significantly greater impact, although whether these warrant the additional investment likely necessary for implementation at scale needs to be investigated. Personalised technology implementation of moderate duration and intensity had similar positive effects to that of stronger duration and intensity, although further research is needed to confirm this. Implications for practice and/or policy: The inclusion of more adaptive personalisation features in technology-assisted learning environments can lead to greater learning gains. Personalised technology approaches featuring moderate personalisation may also yield learning rewards. While it is not known whether personalised technology can be scaled in a cost-effective and contextually appropriate way, there are indications that this is possible. The appropriateness of teachers integrating personalised approaches in their practice should be explored given ‘supplementary’ uses of personalised technology (ie, additional sessions involving technology outside of regular instruction) are common.

AB - Digital technology offers the potential to address educational challenges in resource-poor settings. This meta-analysis examines the impact of students' use of technology that personalises and adapts to learning level in low- and middle-income countries. Following a systematic search for research between 2007 and 2020, 16 randomised controlled trials were identified in five countries. Studies involved 53,029 learners aged 6–15 years. Coding examined learning domain (mathematics and literacy); personalisation level and delivery; technology use; and intervention duration and intensity. Overall, technology-supported personalised learning was found to have a statistically significant—if moderate—positive effect size of 0.18 on learning (p = 0.001). Meta-regression reveals how more personalised approaches which adapt or adjust to learners' level led to significantly greater impact (an effect size of 0.35) than those only linking to learners' interests or providing personalised feedback, support, and/or assessment. Avenues for future research include investigating cost implications, optimum programme length, and teachers' role in making personalised learning with technology effective. Practitioner notes What is already known about this topic? Promoting personalised learning is an established aim of educators. Using technology to support personalised learning in low- and middle-income countries (LMICs) could play an important role in ensuring more inclusive and equitable access to education, particularly in the aftermath of COVID-19. There is currently no rigorous overview of evidence on the effectiveness of using technology to enable personalised learning in LMICs. What this paper adds? The meta-analysis is the first to evaluate the effectiveness of technology-supported personalised learning in improving learning outcomes for school-aged children in LMICs. Technology-supported personalised learning has a statistically significant, positive effect on learning outcomes. Interventions are similarly effective for mathematics and literacy and whether or not teachers also have an active role in the personalisation. Personalised approaches that adapt or adjust to the learner led to significantly greater impact, although whether these warrant the additional investment likely necessary for implementation at scale needs to be investigated. Personalised technology implementation of moderate duration and intensity had similar positive effects to that of stronger duration and intensity, although further research is needed to confirm this. Implications for practice and/or policy: The inclusion of more adaptive personalisation features in technology-assisted learning environments can lead to greater learning gains. Personalised technology approaches featuring moderate personalisation may also yield learning rewards. While it is not known whether personalised technology can be scaled in a cost-effective and contextually appropriate way, there are indications that this is possible. The appropriateness of teachers integrating personalised approaches in their practice should be explored given ‘supplementary’ uses of personalised technology (ie, additional sessions involving technology outside of regular instruction) are common.

KW - computer-assisted learning

KW - learning outcomes

KW - low- and middle-income

KW - meta-analysis

KW - personalisation

KW - personalised adaptive learning

UR - http://www.scopus.com/inward/record.url?scp=85106330783&partnerID=8YFLogxK

U2 - 10.1111/bjet.13116

DO - 10.1111/bjet.13116

M3 - Review article

AN - SCOPUS:85106330783

JO - British Journal of Educational Technology

JF - British Journal of Educational Technology

SN - 0007-1013

ER -